Electric mining shovels (EMS) are large electro-mechanical excavators commonly used in open-cut mining to load haul trucks. They are critical production units at most open-cut mine sites and there is an ongoing need to improve their productivity. FIG. 1 illustrates schematically one of these shovels.
It has been recognized since the 1950s that the capability to measure the weight of material collected by a mining shovel (or other excavator) at each dig can enhance safety and facilitate productivity improvement. The present invention is directed to ideas and methods from the field of optimal state and parameter estimation to address the problem of payload weighing for electric mining shovels.
One example of how knowledge of payload can be used to improve safety and productivity is by reducing the frequency of overloading of haul trucks. Truck overloading comes about primarily because mine sites perceive productivity benefits in loading the maximum possible amount of material into each truck. Most mine sites continue to favor the intuitive argument that pushing the limits of rated capacity increases operational efficiency. Moreover, they perceive the benefits are sufficiently great to pursue this objective aggressively. It is, for example, common practice to award operators performance bonuses if sufficient trucks are loaded within an ‘optimal’ band close to the truck capacities.
Truck overloading reduces safety, reduces the life of the truck, and increases down-time and the maintenance costs. To discourage overloading, maintenance and warranty contracts between a mine operator and a truck manufacturer, nowadays, will usually include a penalty clause that makes it mandatory to stop and dump the load where the truck stands if the scales on-board the truck show it to be overloaded. A dumped load results in a direct productivity loss that is compounded by the costs associated with having to rework the dumped material.
Modern trucks are fitted with on-board scales that are capable of monitoring the weight of material in their trays. These systems typically use suspension strut pressure strategies, and can provide weights accurate to plus or minus 3-5% when calibrated. It is generally accepted, however, that these on-board truck scales become accurate only after the truck is in motion, typically when second gear is reached on flat road. The weights reported at the loading point are generally held to be accurate only to plus or minus 20%, due to the influence of mechanical infelicities such as strut seal friction and strut rod bending in combination with other factors which cause the suspension to lock, such as rock spillage around the loading area and uneven distribution of material in the truck tray.
It normally takes four passes of a large modern electric shovel (with a nominal payload of approximately 80 t) to load a haul truck with a 300 t capacity. To manage the loading process, operators currently use information provided by truck scales (which are known to give poor estimates at the load point) in combination with their judgement. The main motivation for the development of on-shovel payload weighing is the belief that the risk of overloading can be better managed if the shovel operator knows, after each digging pass, how much material is in the bucket, together with the remaining capacity, of the truck tray. This information should empower making a ‘go’ or ‘no go’ decision prior to the final dig pass being dumped into the truck.
Other benefits of payload monitoring are envisaged. For example, giving operators real-time payload weights provides a source of feedback that can help them achieve better, more consistent, operation of the machine. There is, moreover, potential value in being able to monitor shovel performance on a cycle-by-cycle basis; the amount of material collected at each pass is widely held as a key performance indicator.
There have been several previous attempts to develop mining shovel payload systems and several commercial systems are currently available. While manufacturers typically quote accuracy to ±2%, the industry widely accepts that none of these systems can reliably achieve this. For a shovel with a nominal 80 t payload, worst case errors are likely to be as large as ±20 t.
Background work relevant to the payload estimation problem includes
(i) previously developed methods specifically applicable to electric mining shovels,
(ii) methods for estimating payload on other mining equipment such as hydraulic excavators and draglines or cognate equipment such as construction cranies, and
(iii) methods proposed in the robotics literature for the identification of the inertial parameters of robotic links and payload.
Methods for determining payloads on mining machines go back to the 1950s. These systems sometimes involved ingenious means for arriving at an estimate of payload, including analog computations using electrical and fluidic circuits. To the inventor's knowledge none of these systems proved successful; certainly none are in current wide spread use today. Most of the approaches in these early patents have been made redundant by the availability, circa 1980, of microprocessors that could be packaged for deployment on mining equipment. A starting assumption for this work is that any future practical system for payload estimation will be based off microprocessor technology and, accordingly, this review is limited to methods intended for computer-based implementation.
Payload Estimation for Electric Mining Shovels
Chang et al. in U.S. Pat. No. 6,225,574 use a static moment balance on the dipper handle assembly to compute payload. The method equates the moment applied to the dipper and handle assembly by gravity to that applied by the hoist ropes. By careful choice of coordinate frames, the equations are formed so as to be functions of dipper position and hoist rope force only, and are independent of the force applied by the crowd drive. This algorithm is known to be the basis for several commercial payload systems.
The hoist rope force is obtained by measuring the armature current and multiplying it by the motor torque constant and transmission ratios. The gravitation force is assumed to act through a known point (the centre-of-mass of the dipper and handle assembly) allowing the determination of an effective force. This force is then divided by the gravitational acceleration constant to give payload mass. Several estimates of payload can be made during the swing phase of the shovel cycle using this approach and these are averaged to determine the final payload estimate.
Chang et al. report that this method performs adequately when the shovel is stationary but gives poor results when in motion. They attempt to overcome this limitation by assigning a ‘fuzzy’ confidence factor between zero and one that weights each payload estimate in the overall sum. The calculation of these confidence factors is based on the observed speed and acceleration of the dipper at the time of measurement. The higher the speed or acceleration, the lower the confidence value; the lower the velocity and acceleration, the higher the confidence value. Confidence limits are used to cull outliers before the final payload estimate is computed as a weighted average of the instantaneous payload estimates.
This weighted-average approach appears to be an ad hoc attempt to deal with the forces resulting from motion of the machine. A mining shovel is a dynamic machine for which the motion on each drive is intermittent and predictable only in its gross character. An open question is the extent to which inertial forces associated with the acceleration and velocity of the dipper influence estimates of payload mass. This includes the centripetal forces acting when the shovel is swinging.
The method of Chang et al. also assumes that the payload centre of mass is invariant and is fixed at the centre of mass of the bucket for all payload sizes. The validity of this assumption is not tested and the sensitivity of payload estimates to errors in payload centre-of-mass is not reported. Moreover, no explicit attempt is made to compensate for drive friction or other sources of loss in arriving at estimates.
The payload estimation method described by Radomilovich in U.S. Pat. No. 4,677,579 uses a dynamic model that accounts for conservative and non-conservative effects in the hoist and crowd systems. The description of the approach includes the discussion of corrections for the inertial forces associated with the rotational inertia of the drive motors and the reduction gear train, the stretch of the hoist ropes, and the inertia of the bucket. Non-conservative losses due to friction and motor inefficiencies are also mentioned. Radomilovich uses a force balance similar to that of Chang et al., from which payload is estimated. The algorithm, as described, ignores several potentially important factors:                Swing motions are not included. Centrifugal and gyroscopic forces acting on the system are therefore neglected.        The algorithm requires motor accelerations, which are obtained by differentiating motor speed. Motor speed is determined from measurements of armature voltage and current using the steady-state, electrical, motor equations. The differentiation process amplifies noise in the speed signals. The difficulty of obtaining low noise acceleration signals argues for a methodology that does not require inferred measurement of the acceleration.        Motor position is calculated by integrating motor speed. As the armature voltage and current are noisy signals, the variance of the inferred position will grow unboundedly with time. In practice this method would require the shovel kinematics to be constantly recalibrated. This limitation can, of course, be overcome by direct position sensing of the motors.        
The approach of Blair et al. in U.S. Pat. No. 4,809,794 is based on the observation that, in principle, knowing the co-ordinates of the centre of mass of the dipper allows determination of the strain that the weight of the dipper and its contents induce in any part of the boom or the support structure. Conversely, knowing the strain in a selected part of the support structure and the location of the center-of-mass of the dipper allows the weight of the dipper and its contents to be calculated. They propose the fitting of strain gauges to the support structure (on the A-frame structure that supports the boom) together with the use of experimentally or analytically determined influence coefficients, that are functions of dipper position, to relate measured strain to weight of the dipper, handle, and payload. A large part of this patent is given over to describing logic to establish when the shovel is swinging to the dump location, and there is comparatively little description of how the influence coefficients relating weight to strain might be determined. The algorithm averages the weights obtained at each sample over some part of this swing phase. The approach assumes a location for the payload mass-center and does not account for the dynamic loads associated with motion. The authors note that it is preferable to exclude from the averaging step, a number of the weights calculated at the start and end of the swing when these dynamic loads are often at their highest.